{"title":"DriveCP: Occupancy-Assisted Scenario Augmentation for Occluded Pedestrian Perception Based on Parallel Vision","authors":"Songlin Bai;Yunzhe Wang;Zhiyao Luo;Yonglin Tian","doi":"10.1109/JRFID.2024.3392152","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392152","url":null,"abstract":"Diverse and large-high-quality data are essential to the deep learning algorithms for autonomous driving. However, manual data collection in intricate traffic scenarios is expensive, time-consuming, and hard to meet the requirements of quantity and quality. Though some generative methods have been used for traffic image synthesis and editing to tackle the problem of manual data collection, the impact of object relationships on data diversity is frequently disregarded in these approaches. In this paper, we focus on the occluded pedestrians within complex driving scenes and propose an occupancy-aided augmentation method for occluded humans in autonomous driving denoted as “Drive-CP“, built upon the foundation of parallel vision. Due to the flourishing development of AI Content Generation (AIGC) technologies, it is possible to automate the generation of diverse 2D and 3D assets. Based on AIGC technologies, we can construct our human library automatically, significantly enhancing the diversity of the training data. We experimentally demonstrate that Drive-CP can generate diversified occluded pedestrians in real complex traffic scenes and demonstrate its effectiveness in enriching the training set in object detection tasks.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application Research of Parameter Uncertainty Optimization Method in Steering Detection and Correction System","authors":"Jiahao Yang;Ming Xu;Longhua Ma;Fangle Chang;Wenxiang Wu","doi":"10.1109/JRFID.2024.3392444","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392444","url":null,"abstract":"A novel heading angle detection and compensation method is presented with the aim of addressing the navigation and localization accuracy challenges that unmanned robots encounter in their daily inspection jobs, thereby significantly raising the bar for smart port building and promoting the development of ports of superior quality. The Extended Kalman Filter (EKF) algorithm and a Global Navigation Satellite System (GNSS) Inertial Navigation System (INS)/Magnetometer combination navigation technology form the basis of this strategy. The suggested deviation detection and compensating method greatly enhances the navigation system’s performance when compared to the conventional EKF algorithm. Furthermore, we improved the navigation system’s ability to adapt to complex surroundings and sudden changes by adding the Particle Swarm Optimization (PSO) algorithm to the process. This allowed us to further optimize the system parameters based on the original innovation. This development is critical to enhancing unmanned robot navigation accuracy at smart ports and providing robust technical support for the growth of port automation and intelligence.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Personalized and Differentially Private Federated Learning for Anomaly Detection of Industrial Equipment","authors":"Zhen Zhang;Weishan Zhang;Zhicheng Bao;Yifan Miao;Yuru Liu;Yikang Zhao;Rui Zhang;Wenyin Zhu","doi":"10.1109/JRFID.2024.3390142","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3390142","url":null,"abstract":"Federated learning is a distributed machine learning approach that achieves collaborative training while protecting data privacy. However, in distributed scenarios, the operational data of industrial equipment is dynamic and non-independently identically distributed (non-IID). This situation leads to poor performance of federated learning algorithms in industrial anomaly detection tasks. Personalized federated learning is a viable solution to the non-IID data problem, but it is not effective in responding to dynamic environmental changes. Implementing directed updates to the model, thereby effectively maintaining its stability, is one of the solutions for addressing dynamic challenges. In addition, even though federated learning has the ability to protect data privacy, it still has the risk of privacy leakage due to differential privacy attacks. In this paper, we propose a personalized federated learning based on hypernetwork and credible directed update of models to generate stable personalized models for clients with non-IID data in a dynamic environment. Furthermore, we propose a parameter-varying differential privacy mechanism to mitigate compromised differential attacks. We evaluate the capability of the proposed method to perform the anomaly detection task using real air conditioning datasets from three distinct factories. The results demonstrate that our framework outperforms existing personalized federated learning methods with an average accuracy improvement of 11.32%. Additionally, experimental results demonstrate that the framework can withstand differential attacks while maintaining high accuracy.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco Lestini;Gaetano Marrocco;Cecilia Occhiuzzi
{"title":"RFID-Based Reconfigurable Electromagnetic Devices","authors":"Francesco Lestini;Gaetano Marrocco;Cecilia Occhiuzzi","doi":"10.1109/JRFID.2024.3390624","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3390624","url":null,"abstract":"Modern wireless communication systems are becoming increasingly necessary, emphasizing the need for electromagnetic devices that can flexibly operate under different conditions, e.g., under power constraints or in hostile environments where scattering objects randomly modify coverage areas and communication links. Due to their ability to dynamically change operating frequency, radiation pattern, bandwidth characteristics, and polarization, reconfigurable objects (especially antennas and backscattering surfaces) have received significant attention in this context. Electromagnetic features can be electronically selected by controlling the bias voltage of tunable elements adequately integrated into the layout. Usually, this is done by employing external programmable controllers that need power sources and wired connections, leading to unusable configurations for several scenarios. Thus, exploring alternative electronic tuning mechanisms becomes essential. This paper proposes RFID-Based Reconfigurable Electromagnetic Devices as a wireless, cost-effective, and low-power solution. The system’s operating principle, potential architectures, and applicability in practical scenarios are presented. Theoretical and experimental analysis validate the proposed architecture, whose capabilities are finally demonstrated by prototyping and testing two reconfigurable antenna arrays.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Directional Energy-Efficient Metasurface-Backed RFID Reader Antenna for Minimizing Tag-Detection Uncertainty in IoT Networks","authors":"Chandni Bajaj;Dharmendra Kumar Upadhyay;Sachin Kumar;Binod Kumar Kanaujia","doi":"10.1109/JRFID.2024.3389737","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3389737","url":null,"abstract":"In this paper, a 2.45/5.8 GHz circularly polarized RFID antenna backed with a dual-band artificial magnetic conductor (AMC) is presented for tagged-object detection in Internet of things (IoT) networks. RFID systems require a long-range reader to maintain energy-efficient communication with the tagged devices. An efficient reader antenna is presented to increase the interrogation distance of the reader, and decreasing the uncertainty of tagged-object detection. The proposed RFID antenna consists of two dipole pairs, are printed on both sides of the substrate to obtain 2.45 GHz and 5.8 GHz bands, connected by feed delay lines in a cross-dipole arrangement. To increase the range of the reader, the cross-dipole antenna is supported by a \u0000<inline-formula> <tex-math>$5times5$ </tex-math></inline-formula>\u0000 AMC surface placed \u0000<inline-formula> <tex-math>$0.042{lambda }_{0}$ </tex-math></inline-formula>\u0000 beneath the antenna, where \u0000<inline-formula> <tex-math>${lambda }_{0}$ </tex-math></inline-formula>\u0000 is the wavelength calculated at the lowest resonant frequency. In measurement, the metasurface backing results in gain enhancements of 4.1 dBi in the 2.45 GHz band and 4.6 dBi in the 5.8 GHz band, which improves the read range of the reader. The measurement also shows an improvement in RFID band coverage in terms of impedance bandwidth, 2.32–2.57 GHz in the 2.45 GHz band and 5.48-6 GHz in the 5.8 GHz band, reducing tag-detection error. The axial ratio bandwidth of the reader antenna configuration is 2.385–2.485 GHz and 5.77–5.88 GHz in the 2.45 GHz and 5.8 GHz bands, respectively.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Joint Design of Self-Tuning UHF RFID Antenna and Microfluidic Channel for Liquid Sensing","authors":"Giulio Maria Bianco;Gaetano Marrocco","doi":"10.1109/JRFID.2024.3389870","DOIUrl":"10.1109/JRFID.2024.3389870","url":null,"abstract":"Microfluidic has been an enabling technology for over a decade, particularly in the field of medical and wearable devices, allowing for the manipulation of small amounts of fluid in confined spaces. Micro-channels can also be used for wireless sensing thanks to the variations in antenna properties when the fluid flows near it. However, up to now, microfluidic channels and sensing antennas have always been designed separately; instead, since the liquid flow and the antenna geometry both contribute to the overall performance, they should be considered simultaneously when optimizing the antenna-microfluidic system. In this paper, the joint design of the antenna and microfluidic channels is investigated for liquid quantification. Self-tuning RFID microchips are exploited to minimize communication degradation due to the increase of lossy liquid amount over the sensing antenna while digitalizing the impedance mismatch itself. To experimentally corroborate the joint design technique, two different geometries are obtained and prototyped starting from a given antenna-microfluidic layout by setting different goals for an optimization function. The two flexible RFID prototypes returned performance in agreement with the simulated ones, achieving a maximum sensitivity of about 20 units of the digital metric per milligram increase of water.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lin Wang;Fenghua Zhu;Hui Zhang;Gang Xiong;Yunhu Huang;Dewang Chen
{"title":"MSSINet: Real-Time Segmentation Based on Multi-Scale Strip Integration","authors":"Lin Wang;Fenghua Zhu;Hui Zhang;Gang Xiong;Yunhu Huang;Dewang Chen","doi":"10.1109/JRFID.2024.3389088","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3389088","url":null,"abstract":"Semantic segmentation plays a fundamental role in computer vision, underpinning applications such as autonomous driving and scene analysis. Although dual-branch networks have marked advancements in accuracy and processing speed, they falter in the context extraction phase within the low-resolution branch. Traditionally, square pooling is used at this juncture, leading to the oversight of stripe-shaped contextual information. In response, we introduce a novel architecture based on a deep aggregation pyramid, engineered for both real-time processing and precise segmentation. Central to our approach is a pioneering contextual information extractor designed to expand the effective receptive fields and fuse multi-scale context from low-resolution feature maps. Additionally, we have developed a feature fusion module to enhance the integration and differentiation of high-level semantic information across branches. To further refine the fidelity of segmentation, we implement dual deep supervisions within the high-resolution branchs intermediate layer, concentrating on boundary delineation and global features to enrich spatial detail capture. Our comprehensive experimental analysis, conducted on the Cityscapes and CamVid datasets, affirms MSSINets superior performance, showcasing its competitiveness against existing leading methodologies across a variety of scenarios.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erbo Shen;Weidong Yang;Xuyu Wang;Bo Kang;Shiwen Mao
{"title":"TagSense: Robust Wheat Moisture and Temperature Sensing Using RFID","authors":"Erbo Shen;Weidong Yang;Xuyu Wang;Bo Kang;Shiwen Mao","doi":"10.1109/JRFID.2024.3389868","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3389868","url":null,"abstract":"Grain is a major source of food, while grain security has been considered as a strategic issue in many countries. Temperature and moisture as the two key properties affect the quality of stored grain. Most existing approaches for sensing these properties are expensive, time-consuming, and are difficult to deploy. In this paper, we design a TagSense system to sense the temperature and moisture level of stored wheat using commodity RFID devices, where tag’s impedance is exploited as a feature for target sensing at a low cost. Since impedance is sensitive to the signal propagation distance and incidence angle, we propose a distance-independent algorithm and an angle-agnostic approach to mitigate the impact of distance and angles on the sensing performance. Our extensive experiment results demonstrate that TagSense can achieve a satisfactory sensing performance at any distance and any angle within the sensing range.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rakiba Rayhana;Ling Bai;Gaozhi Xiao;Min Liao;Zheng Liu
{"title":"Digital Twin Models: Functions, Challenges, and Industry Applications","authors":"Rakiba Rayhana;Ling Bai;Gaozhi Xiao;Min Liao;Zheng Liu","doi":"10.1109/JRFID.2024.3387996","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3387996","url":null,"abstract":"In the rapidly evolving landscape of Industry 4.0, digital twins have emerged as a transformative technology across various industrial sectors. This paper presents a comprehensive, in-depth review of digital twin models in terms of the concept and evolution, fundamental components and frameworks, and existing digital twin models based on their functionalities. The paper also discusses how the existing digital twin models are used/adopted in different industries and highlights the existing challenges and potential solutions to address the current issues. This paper aims to provide researchers and industry professionals with a clear insight into the unique benefits and applications of different digital twin models. This review will help to comprehend their significance for specific industrial purposes and foster the advancement of state-of-the-art techniques in this field.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gaofeng Zhu;Zhixue Wang;Fenghua Zhu;Gang Xiong;Zheng Li
{"title":"Small Object Recognition Algorithm Based on Hybrid Control and Feature Fusion","authors":"Gaofeng Zhu;Zhixue Wang;Fenghua Zhu;Gang Xiong;Zheng Li","doi":"10.1109/JRFID.2024.3384483","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3384483","url":null,"abstract":"Drone detection plays a key role in various fields, but from the perspective of drones, factors such as the size of the target, interference from different backgrounds, and lighting affect the detection effect, which can easily lead to missed detections and false detections. To address this problem, this paper proposes a small target detection algorithm. First, the hybrid control of attention mechanism and a convolutional module (HCAC) are used to effectively extract contextual details of targets of different scales, directions, and shapes, while relative position encoding is used to associate targets with position information. Secondly, in view of the small size characteristics of small targets, a high-resolution detection branch is introduced, the large target detection head and its redundant network layers are pruned, and a multi-level weighted feature fusion network (MWFN) is used for multi-dimensional fusion. Finally, the WIoU loss is used as a bounding box regression loss, combined with a dynamic non-monotonic focusing mechanism, to evaluate the quality of anchor boxes so that the detector can handle anchor boxes of different qualities, thus improving the overall performance. Experiments were conducted on the UAV aerial photography data set VisDrone2019. The results showed that the accuracy of P increased by 9.0% and MAP by 9.8%, with higher detection results.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}